Machine learning based tuning of radio frequency apparatuses

ABSTRACT

Methods, systems, and/or devices for tuning the configuration settings of one or more RF apparatuses are provided. Various embodiments described herein regard a system that includes a radio frequency apparatus configured to operate based on a plurality of possible configuration settings to generate an output signal that is characterized by a performance metric. The system can also include a tuner that employs a machine learning engine having a training stage and an inference stage. The inference stage can be configured to, based on a machine learning model, search the possible configuration settings for a target configuration setting that results in the performance metric meeting defined bounds of an optimization threshold value.

TECHNICAL FIELD

The field relates to systems and computer-implemented methods forutilizing machine learning to tune operation parameters for one or moreradio frequency apparatuses.

RELATED ART

Mixed-signal and radio frequency (“RF”) apparatus are commonly used fordigital communication. The performance of these apparatuses is subjectto variations caused by intrinsic properties of the circuit componentsand/or assembly conditions. Consequently, RF apparatuses are designedwith variable components that can be tuned to optimize performance. Forexample, one or more component configuration settings can be tuned sothat the RF apparatus achieves desired outputs and/or measurements, asdefined by a prescribed set of objectives (e.g., such as matched powergain and/or phase).

The RF apparatus's configuration settings can encompass a vastcombinatorial space of potential parameter values regarding, forexample: frequencies of operation, power levels, transistor bias levels,operational temperatures, a combination thereof, and/or the like. Thus,tuning the RF apparatus involves identifying one or more specificconfiguration settings from the combinatorial space that results in thedesired performance.

Traditionally, the tuning is performed by subject matter experts, whorely on experience and industry knowledge to search for the optimalconfiguration settings via trial and error. However, manual tuningoperations require a deep technical understanding or knowledge of theapparatus and its behavior, thereby necessitating product-specifictraining. Thus, manual tuning approaches are limited in theirapplication, time consuming, costly, and/or inefficient. Attempts havebeen employed to automate RF apparatus tuning via a linear searchprocess; however, typical automation approaches are still time consumingand not effective when the apparatus exhibits non-linear performanceresults. In addition, typical tuning approaches are not able to respondquickly to variations over time in the manufacturing process or changesin the underlying product technology.

SUMMARY

The following presents a summary to provide a basic understanding of oneor more embodiments described herein. This summary is not intended toidentify key or critical features, or to delineate any scope ofparticular embodiments and/or claims. The sole purpose of this summaryis to present concepts in a simplified form as prelude to the moredetailed description that is presented below. In one or more embodimentsdescribed herein, systems, computer-implemented methods, apparatuses,and/or computer program products that can tune the configurationsettings of one or more RF apparatuses are described.

In an embodiment, a system includes a radio frequency apparatus that canbe configured to operate based on a plurality of possible configurationsettings to generate an output signal that is characterized by aperformance metric. The system can further include a tuner that canemploy a machine learning engine having a training stage and aninference stage. The inference stage can be configured to, based on amachine learning model, search the possible configuration settings for atarget configuration setting that results in the performance metricmeeting defined bounds of an optimization threshold value.

In another embodiments, the system also includes a tester that cancontrol operation of the radio frequency apparatus based on a pluralityof test configuration settings identified by the tuner. The targetconfiguration setting can be from the plurality of test configurationsettings. In one aspect, the radio frequency apparatus can be anamplifier, filter, digital signal processor, radio frequency integratedcircuit, micro-electro-mechanical system filter, and/or monolithicmicrowave integrated circuit. In another aspect, the plurality of testconfiguration settings can modulate at least one parameter of the outputsignal and/or operating parameter of the radio frequency apparatus.Further, the at least one parameter of the output signal can include:amplitude variation, rise time, fall time, pulse width, output power,in-band spectral emissions, out-of-band spectral emissions, error vectormagnitude, and/or a combination thereof. Also, the at least oneoperating parameter of the radio frequency apparatus can include: filtercoefficient, output power, and/or a combination thereof.

In one or more embodiments, the performance metric can be a function ofperformance evaluation data that characterizes the output signal and/orthe operating parameter of the radio frequency apparatus. In one aspect,the tuner can determine the performance metric by comparing theperformance evaluation data to a target performance dataset. In anotheraspect, the tester can determine the performance metric by executing aloss function algorithm. Also, the defined bounds of the optimizationthreshold can be a range less than or equal to a defined loss value. Ina further aspect, the loss function algorithm can be a correlation-basedloss function algorithm or an error-based loss function algorithm.

In another embodiment, the machine learning engine can execute aBayesian optimization algorithm to identify the plurality of testconfiguration settings based on historic performance metrics thatcharacterize previous output signals generated by the radio frequencyapparatus in response to operations controlled by the tester.

In a further embodiment, the tester can be a computer executablecomponent stored in a computer readable storage medium comprised withinthe radio frequency apparatus.

In a still further embodiment, the tester can send the historicperformance metrics to the tuner and receives the plurality of testconfiguration settings from the tuner via a cloud computing environment.

In a still further embodiment, the machine learning engine can includecomputer executable components that include an initialization componentthat selects an initial configuration setting from the plurality ofpossible configuration settings. Also, the system further can comprisesa tester that controls operation of the radio frequency apparatus inaccordance with the initial configuration setting. In one aspect, theinitialization component can randomly select the initial configurationsetting. In another aspect, the computer executable components canfurther include a model update components that tunes a hyperparameter ofthe machine learning model based on the performance metric thatcharacterizes the output generated from a previously testedconfiguration setting. In a further aspect, the computer executablecomponents further include a candidate component that selects a testconfiguration setting based on the tuned machine learning model. Thetester can further control the operation of the radio frequencyapparatus in accordance with the test configuration setting.

In another embodiment, the target configuration setting can optimize theradio frequency apparatus for use in a time-divisional multiple accessdigital communications network.

Another embodiment is drawn to a computer-implemented method for tuninga configuration setting of a radio frequency apparatus. Thecomputer-implemented method can include applying a machine learningmodel to generate a test configuration setting for the radio frequencyapparatus. Further, the computer-implemented method can includegenerating performance evaluation data by operating the radio frequencyapparatus with the test configuration setting. Additionally, thecomputer-implemented method can include comparing the performanceevaluation data to a target performance dataset to determine whether thetest configuration setting is an optimal configuration setting for adefined objective.

A still further embodiment is drawn to a computer program product fortuning configuration settings of a radio frequency apparatus. Thecomputer program product includes a computer readable storage mediumhaving program instructions embodied therewith. The program instructionscan be executable by one or more processors to cause the one or moreprocessors to control an operation of a radio frequency apparatus usingan initial configuration setting. Also, the program instructions cancause the one or more processors to update a machine learning modelbased on performance evaluation data characterizing the operation of theradio frequency apparatus. Further, the program instructions can causethe one or more processors to determine a test configuration setting forthe radio frequency apparatus based on a prediction generated by themachine learning model regarding a second operation of the radiofrequency apparatus using the test configuration setting.

Further embodiments, features, and advantages of the invention, as wellas the structure and operation of the various embodiments of theinvention are described in detail below with reference to accompanyingdrawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a diagram of an example, non-limiting system that cantune the configuration settings of one or more RF apparatuses inaccordance with one or more embodiments described herein.

FIG. 2 illustrates a diagram of the example, non-limiting systemcomprising one or more machine learning models that can be utilized byone or more machine learning engines to analyze a combinatorialparameter space characterizing potential configuration settings for oneor more RF apparatuses in accordance with one or more embodimentsdescribed herein.

FIG. 3 illustrates a diagram of an example, non-limiting tuningoperation that can be implemented by one or more apparatuses, systems,and/or computer-implemented methods to tune one or more RF apparatusesvia a sequential model-based optimization (“SMBO”) technique inaccordance with one or more embodiments described herein.

FIG. 4 illustrates a diagram of an example, non-limiting machinelearning engine that can execute a Bayesian optimization algorithm todetermine test configuration settings for tuning one or more RFapparatuses in accordance with one or more embodiments described herein.

FIGS. 5-6 illustrate flow diagrams of example, non-limitingcomputer-implemented methods that can be implemented by one or moreapparatuses and/or systems to tune one or more RF apparatuses inaccordance with one or more embodiments described herein.

FIGS. 7A-7B illustrate diagrams of example, non-limiting RF apparatusesthat can comprise one or more on-board and/or remote testers and/ortuners to facilitate one or more tuning operations to optimizeperformance in accordance with one or more embodiments described herein.

FIG. 8 illustrates a diagram of the example, non-limiting system inwhich multiple concurrent tuning operations can be performed based oneach other in accordance with one or more embodiments described herein.

FIG. 9 illustrates a diagram of example, non-limiting graphsdemonstrating the efficacy of one or more autonomous tuning operationsin optimizing the performance of an RF apparatus in accordance with oneor more embodiments described herein.

DETAILED DESCRIPTION

The following detailed description is merely illustrative and notintended to limit the scope and/or use of embodiments. Furthermore,there is no intention to be bound by any expressed or impliedinformation presented in the preceding Background or Summary sections,or in the following Detailed Description section.

One or more embodiments are now described with reference to theDrawings, where like referenced numerals are used to refer to likeelements throughout. In the following Detailed Description, for purposedof explanation, numerous specific details are set forth in order toprovide a more thorough understanding of the one or more embodiments.However, it is evident that one or more embodiments can be practicedwithout these specific details.

Embodiments refer to illustrations described herein with reference toparticular applications. It should be understood that the presentdescription is not limited to the embodiments. Those skilled in the artwith access to the teachings provided herein will recognize additionalmodifications, applications, and embodiments within the scope there andadditional fields in which the embodiments would be of significantutility.

Various embodiments described herein can regard tuning one or more RFapparatuses using machine learning. For example, one or more machinelearning models can be employed to search a space of combinatorialconfiguration settings to identify settings that are predicted toachieve optimal performance metrics for the RF apparatus. Further, theRF apparatus can be operated with the identified settings, whereupon theperformance data can be evaluated and used to update the machinelearning model to improve the accuracy of subsequent predictions. Forinstance, the one or more machine learning models can operate inconjunction with one or more automated testing components that cancontrol operation of the RF apparatus (e.g., change operationalparameters in accordance with configuration settings identified by themachine learning model) and collect performance evaluation data to trainand/or update the machine learning models. In one or more embodiments,the tuning process can comprise multiple iterations of: employing themachine learning model to identify test configuration settings;evaluating performance data associated with operating the one or more RFapparatuses with the test configuration settings; and updating themachine learning model based on the performance data evaluation.

Additionally, the one or more machine learning models can search acombinatorial configuration settings search space that includesparameter values for multiple RF apparatuses that can work inconjunction with each other. For example, the one or more tuningoperations described herein can be employed to optimize complex and/ordynamic systems comprising multiple RF apparatuses working in tandem,such as systems comprising a ground station, a satellite, and/or a userterminal to conduct telecommunications. For instance, the optimalperformance of a first RF apparatus can be based on the performance of asecond RF apparatus (and/or vice versa), where the one or more machinelearning models can analyze the performance data of both RF apparatusesin searching the combinatorial configuration settings space forcontrolling the parameters of the first RF apparatus and/or the secondRF apparatus.

In one or more embodiments, the one or more machine learning models canbe employed via a cloud computing environment to facilitate the tuningoperations. For example, one or more automated testing components cancommunicate with, and/or share data with, the one or machine learningmodels via one or more wireless networks. Further, a cloud computingenvironment can be employed to facilitate a tuning system in which amachine learning model is operatively coupled to multiple testingcomponents to tune the configuration settings for multiple RF apparatus.Thereby, the machine learning model can leverage lessons learned fromtuning a first RF apparatus in tuning a second RF apparatus. In someembodiments, the one or more machine learning models and/or testingcomponents can be housed within, and/or integrated with, the one or moreRF apparatuses to facilitate a self-tuning operation.

The computer processing systems, computer-implemented methods, computerprogram products, and/or computer apparatuses described herein employhardware and/or software to solve problems that are highly technical innature (e.g., optimizing RF apparatus configuration settings from a vastcombinatorial search space), which are not abstract and cannot beperformed by the mental acts of a human. For example, an individual, oreven a plurality of individuals, cannot search a vast combinatorialconfiguration settings space with the efficiency described herein.Additionally, one or more embodiments described herein can constituteone or more technical improvements over conventional tuning processes byutilizing machine learning models to account for parameter relationshipsthat may be non-linear in nature. Further technical improvementsachieved by the various embodiments described herein include: tuning formultiple objectives (e.g., prioritized objective tuning); performingmultiple tuning operations in parallel; and/or utilizing transferlearning techniques to leverage lessons learned from similar knowledgedomains.

As used herein, the term “machine learning” can refer to an applicationof artificial intelligence technologies to automatically and/orautonomously learn and/or improve from an experience (e.g., trainingdata) without explicit programming of the lesson learned and/or improvedupon. Various system components described herein can utilize machinelearning (e.g., via supervised, unsupervised, and/or reinforcementlearning techniques) to perform tasks such as classification,regression, and/or clustering. Execution of machine learning tasks canbe facilitated by one or more machine learning models trained on one ormore training datasets in accordance with one or more modelconfiguration settings.

As used herein, the term “machine learning model” can refer to acomputer model used to facilitate one or more machine learning tasks(e.g., regression and/or classification tasks). For example, a machinelearning model can represent relationships (e.g., causal or correlationrelationships) between parameters and/or outcomes within the context ofa specified domain. For instance, machine learning models can representthe relationships via probabilistic determinations that can be adjusted,updated, and/or redefined based on historic data and/or previousexecutions of a machine learning task. In various embodiments describedherein, machine learning models can simulate a number of interconnectedprocessing units that can resemble abstract versions of neurons. Forexample, the processing units can be arranged in a plurality of layers(e.g., one or more input layers, hidden layers, and/or output layers)connected by varying connection strengths (e.g., which can be commonlyreferred to within the art as “weights”).

Machine learning models can learn through training with one or moretraining datasets; where data with known outcomes in inputted into themachine learning model, outputs regarding the data are compared to theknown outcomes, and/or the weights of the machine learning model areautonomously adjusted based on the comparison to replicate the knownoutcomes. As the one or more machine learning models train (e.g.,utilize more training data), the machine learning models can becomeincreasingly accurate; thus, trained machine learning models canaccurately analyze data with unknown outcomes, based on lessons learnedfrom training data and/or previous executions, to facilitate one or moremachine learning tasks.

Example types of machine learning models can include, but are notlimited to: artificial neural network (“ANN”) models, perceptron (“P”)models, feed forward (“FF”) models, radial basis network (“RBF”) models,deep feed forward (“DFF”) models, recurrent neural network (“RNN”)models, long/short memory (“LSTM”) models, gated recurrent unit (“GRU”)models, auto encoder (“AE”) models, variational AE (“VAE”) models,denoising AE (“DAE”) models, sparse AE (“SAE”) models, markov chain(“MC”) models, Hopfield network (“HN”) models, Boltzmann machine (“BM”)models, deep belief network (“DBN”) models, convolutional neural network(“CNN”) models, deep convolutional network (“DCN”) models,deconvolutional network (“DN”) models, deep convolutional inversegraphics network (“DCIGN”) models, generative adversarial network(“GAN”) models, liquid state machine (“LSM”) models, extreme learningmachine (“ELM”) models, echo state network (“ESN”) models, deep residualnetwork (“DRN”) models, kohonen network (“KN”) models, support vectormachine (“SVM”) models, and/or neural turing machine (“NTM”) models.

As used herein, the term “transfer learning” can refer to one or moremachine learning processes that utilize the knowledge gained fromexecuting a first machine learning task in executing a second machinelearning task. Transfer learning can be utilized to leverage lessonslearned between different knowledge domains and/or between similarmachine learning tasks. For instance, where a target knowledge domainlacks sufficient data to accurately train a machine learning model, apre-trained machine learning model (e.g., pre-trained in anotherknowledge domain that shares similarities with the target knowledgedomain) can be utilized to execute a machine learning task in the targetknowledge domain. In another instance, transfer learning can utilizeoutcomes and/or model configuration settings from a pre-trained machinelearning model to facilitate training another machine learning model inanother knowledge domain.

As used herein, the term “transfer learning model” can refer to one ormore machine learning models that are pre-trained and can be utilized inone or more transfer learning processes. For example, a transferlearning model can be trained to execute a first machine learning task,and utilized to execute, or facilitate execution, of a second, distinctmachine learning task. Transfer learning models can be pre-existingmachine learning models chosen from a library of models. Additionally,transfer learning models can be generated from the combination and/oralteration of one or more pre-existing machine learning models, wherethe transfer learning models can be fine-tuned based on one or morecharacteristics of the new data to be analyzed by the one or moresubject machine learning tasks.

FIG. 1 illustrates a block diagram of an example, non-limiting system100 that can tune adjustable settings of one or more RF apparatuses 102.One or more aspects of system 100 can constitute one or moremachine-executable components that can be embodied within one or morecomputer readable mediums associated with one or more machines. Forexample, one or more machines (e.g., computers, computing devices,virtual machines, and/or the like) can execute the one or moremachine-executable components to perform various operations describedherein.

As shown in FIG. 1 , the system 100 can comprise one or more tuners 103,networks 104, and/or input/output devices 106. The one or more tuners103 can comprise one or more processing units 108 and/or computerreadable storage media 110. In various embodiments, the one or moreprocessing units 108 and computer readable storage media 110 can beoperably coupled by one or more system buses 112. In variousembodiments, the one or more tuners 103 can be, for example: a server, adesktop computer, a laptop, a hand-held computing apparatus, aprogrammable apparatus, a minicomputer, a mainframe computer, anInternet of Things (“IoT”) device, a combination thereof, and/or thelike.

In one or more embodiments, the computer readable storage media 110 canbe distributed across a cloud computing environment and remotelyaccessible (e.g., by the one or more processing units 108) via the oneor more networks 104. The computer readable storage media 110 cancomprise one or more memory units and can store one or more computerexecutable components 114, which can be executed by the one or moreprocessing units 108. The one or more computer executable components 114can comprise, for example, communications component 116 and/or machinelearning engine 118. The system 100 can also comprise one or moretesters 120 and/or data repositories 124. As shown in FIG. 1 , the oneor more input/output devices 106, testers 120, RF apparatuses 102,and/or data repositories 124 can be operatively coupled to the one ormore tuners 103 and/or each other via the one or more networks 104.

The one or more RF apparatuses 102 can be electronic-electrical devicescapable of emitting radio frequency energy (e.g., by radiation,conduction, and/or induction) via circuitry that operates in the radioand/or satellite frequency spectrum (e.g., operating in the L-, Ka-, C-,or Ku-band). Example RF apparatuses 102 can include, but are not limitedto: amplifiers, filters, digital signal processors, radio frequencyintegrated circuits (“RFIC”), micro-electro-mechanical system filters(“MEMS”), monolithic microwave integrated circuits (“MMIC”),multi-channel radios, satellite terminals (e.g., airborne terminals,marine terminals, ground terminals, and/or fixed broadband terminals), acombination thereof, and/or the like. For instance, the one or more RFapparatuses 102 can be user terminals, satellites, and/or gatewaydevices used in a satellite communications system. In accordance withvarious embodiments described herein, the one or more RF apparatuses 102can comprise one or more variable components (e.g., field programmablegrid arrays (“FPGAs”), microcontrollers, integrated circuits,transceivers, radio modems, wireless modems, and/or the like) that canbe adjusted to alter the operation of the one or more RF apparatuses102.

In various embodiments, the one or more RF apparatuses 102 can befilters, amplifiers, and/or other RF apparatuses 102 that can bemanipulated in the digital domain and/or the analog domain. Digitalmanipulation can include the digital settings of IC operatingparameters. For example, a FPGA acting as a DSP can be configured withone or more filter coefficients, such as in a finite impulse response(“FIR”) filter. Digitally controlled amplifiers can have adjustableconfiguration controls that modulate various RF parameters, such as:gain, phase, band flatness, and/or the like. Tunable RF MEMS filters canalso be adjusted and/or reconfigured to change the impedance of acircuit or filter. Additionally, a cascaded system combining one or moreanalog tunable amplifiers, filters, DSPs, RFICs, MMICs, or MEMS can betuned to match individual components or achieve a target operationalperformance for a specific frequency space.

The one or more tuners 103 can utilize machine learning to tune theconfiguration settings of the one or more RF apparatuses 102. The tuningoperations performed by the tuners 103 can adjust the performance of theone or more RF apparatuses 102 to meet one or more defined performanceand/or optimization thresholds (e.g., set by one or more users of thesystem 100 via the one or more input/output devices 106). In accordancewith the various embodiments described further herein, the one or moretuners 103 can employ machine learning models that characterizeprobabilistic relationships between parameter values controlled by theRF apparatus 102 configuration settings. Additionally, the one or moretuners 103 can utilize historic performance data regarding operation ofthe one or more RF apparatuses 102 to improve the accuracy and/orprecision of subsequent tuning operations. In various embodiments, theone or more tuners 103 can tune the configuration settings of the one ormore RF apparatuses 102 such that the one or more RF apparatuses areoptimized to provide a defined level of performance; while minimizingthe number of iterations in the tuning operation. For example, the oneor more tuners 103 can utilize sequential machine learning model-basedBayesian optimization techniques to reduce the number of test operationsrequired during the tuning operation.

In various embodiments, the one or more processing units 108 cancomprise any commercially available processor. For example, the one ormore processing units 108 can be a general purpose processor, anapplication-specific system processor (“ASSIP”), an application-specificinstruction set processor (“ASIPs”), or a multiprocessor. For instance,the one or more processing units 108 can comprise a microcontroller,microprocessor, a central processing unit, and/or an embedded processor.In one or more embodiments, the one or more processing units 108 caninclude electronic circuitry, such as: programmable logic circuitry,FPGA, programmable logic arrays (“PLA”), an IC, and/or the like.

In various embodiments, the one or more computer executable components114 can be program instructions for carrying out one or more operationsdescribed herein. For example, the one or more computer executablecomponents 114 can be, but are not limited to: assembler instructions,instruction-set architecture (“ISA”) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data, source code, object code, acombination thereof, and/or the like. For instance, the one or morecomputer executable components 114 can be written in one or moreprocedural programming languages. Although FIG. 1 depicts the computerexecutable components 114 stored on the one or more tuners 103, thearchitecture of the system 100 is not so limited. For example, the oneor more computer executable components 114 can be stored on one or morecomputer readable storage media 110 that are external to the one or moretuners 103.

The one or more computer readable storage media 110 can include, but arenot limited to: an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, a combination thereof, and/or the like.For example, the one or more computer readable storage media 110 cancomprise: a portable computer diskette, a hard disk, a random accessmemory (“RAM”) unit, a read-only memory (“ROM”) unit, an erasableprogrammable read-only memory (“EPROM”) unit, a CD-ROM, a DVD, Blu-raydisc, a memory stick, a combination thereof, and/or the like. Thecomputer readable storage media 110 can employ transitory ornon-transitory signals. In one or more embodiments the computer readablestorage media 110 can be tangible and/or non-transitory. In variousembodiments, the one or more computer readable storage media 110 canstore the one or more computer executable components 114 and/or one ormore other software applications, such as: a basic input/output system(“BIOS”), an operating system, program modules, executable packages ofsoftware, and/or the like. Also, the one or more of the computerexecutable components 114 described herein can be shared betweenmultiple tuners 103 comprised within the system 100 via the one or morenetworks 104.

As shown in FIG. 1 , the one or more computer executable components 114can comprise a communications component 116 and/or machine learningengine 118. In various embodiments, the communications component 116 canfacilitate data sharing between the one or more tuners 103 and the oneor more RF apparatuses 102, input/output devices 106, testers 120,and/or data repositories 124. For instance, the communications component116 can process data received via the one or more networks 104, andshare the received data with one or more associate computer executablecomponents 114. In one or more embodiments, the communications component116 can be a part of a data communication system.

The machine learning engine 118 can execute one or more machine learningalgorithms to execute a machine learning task, such as a tuningoperation that identifies test configuration settings that are predictedto improve the performance of the one or more RF apparatuses 102. Invarious embodiments, the machine learning engine 118 can comprise: atraining stage (e.g., exemplified by training stage 400 in FIG. 4 ),where one or more machine learning models 206 are trained; and/or aninference stage (e.g., exemplified by inference stage 401 in FIG. 4 ),where the machine learning engine 118 can be configured to, based on themachine learning model, search the possible configuration settings thatcan be implemented by the one or more RF apparatuses 102 for a targetconfiguration setting that enables the one or more RF apparatuses 102 toachieve a defined level of optimal performance.

The one or more networks 104 can comprise one or more wired and/orwireless networks, including, but not limited to: a cellular network, awide area network (“WAN”), a local area network (“LAN”), a combinationthereof, and/or the like. One or more wireless technologies that can becomprised within the one or more networks 104 can include, but are notlimited to: wireless fidelity (“Wi-Fi”), a WiMAX network, a wireless LAN(“WLAN”) network, BLUETOOTH® technology, a combination thereof, and/orthe like. For instance, the one or more networks 104 can include theInternet and/or the Internet of Things (“IoT”). In various embodiments,the one or more networks 104 can comprise one or more transmission lines(e.g., copper, optical, or wireless transmission lines), routers,gateway computers, and/or servers. Further, the one or more tuners 103can comprise one or more network adapters and/or interfaces (not shown)to facilitate communications via the one or more networks 104.

In various embodiments, the one or more input/output devices 106 can beemployed to enter data and/or commands into the system 100. Example datathat can be entered via the one or more input/output devices 106 caninclude, but are not limited to: tuning objectives, optimizationthreshold values and/or measurements, RF apparatus 102 operationconstraints, domain knowledge, RF apparatus 102 specification dataand/or metadata, a combination thereof, and/or the like. For instance,the one or more input/output devices 106 can be employed to initializeand/or control one or more operations of the one or more tuners 103(and/or associate components), testers 120, and/or RF apparatuses 102.In various embodiments, the one or more input/output devices 106 cancomprise and/or display one or more input interfaces (e.g., a userinterface) to facilitate entry of data into the system 100.Additionally, in one or more embodiments the one or more input/outputdevices 106 can be employed to define one or more system 100 settings,parameters, definitions, preferences, thresholds, and/or the like. Also,in one or more embodiments the one or more input/output devices 106 canbe employed to display one or more outputs from the one or more tuners103 and/or query one or more system 100 users. For example, the one ormore input/output devices 106 can send, receive, and/or otherwise sharedata (e.g., inputs and/or outputs) with the one or more tuners 103(e.g., via a direct electrical connection and/or the one or morenetworks 104).

The one or more input/output devices 106 can comprise one or morecomputer devices, including, but not limited to: desktop computers,servers, laptop computers, smart phones, smart wearable devices (e.g.,smart watches and/or glasses), computer tablets, keyboards, touch pads,mice, augmented reality systems, virtual reality systems, microphones,remote controls (e.g., an infrared or radio frequency remote control),stylus pens, biometric input devices, a combination thereof, and/or thelike. Additionally, the one or more input/output devices 106 cancomprise one or more displays that can present one or more outputsgenerated by, for example, the tuner 103. Example displays can include,but are not limited to: cathode tube display (“CRT”), light emittingdiode display (“LED”), electroluminescent display (“ELD”), plasmadisplay panel (“PDP”), liquid crystal display (“LCD”), organiclight-emitting diode display (“OLED”), a combination thereof, and/or thelike. In various embodiments, the one or more input/output devices 106can present one or more outputs of the one or more tuners 103, testers120, and/or RF apparatuses 102 via an augmented reality environment or avirtual reality environment.

The one or more testers 120 can control operations of the one or more RFapparatuses 102 to test configuration settings identified by the one ormore tuners 103. For example, the one or more testers 120 can executeone or more test operations with the one or more RF apparatuses 102. Theone or more test operations can control the one or more RF apparatuses102 in accordance with one or more test configuration settings providedby the one or more tuners 103 and/or one or more task settings (e.g.,included in the task data 304 exemplified in the tuning operation 300 ofFIG. 3 ) defined by the one or more input/output devices 106. The one ormore test configuration settings can define values for one or morevariable components of the one or more RF apparatuses 102. For example,the one or more test configuration settings can define values regarding,but not limited to: voltage, frequency range, filter coefficients, acombination thereof, and/or the like. The one or more task settings candefine one or more tasks to be executed by the one or more RFapparatuses 102 during the test operations. For example, the one or moretask settings can define: one or more operational constraints of thetest operations, one or more task objectives to be accomplished byexecution of the test operations, one or more operation protocols to beexecuted via the test operations, a combination thereof, and/or thelike. In various embodiments, the one or more testers 120 can retrievehistoric task settings from the one or more data repositories 124.

In one or more embodiments, the one or more testers 120 can furthercollect one or more outputs generated by the one or more RF apparatuses102 during the test operations. Based on the one or more collectedoutputs, the one or more testers 120 can generate performance evaluationdata that characterizes the performance quality of the one or more RFapparatuses 102 as a result of operation in accordance with the testconfiguration settings. The one or more outputs collected by one or moretesters 120 can be, for instance: products generated by one or more RFapparatuses 102, one or more measured metrics regarding productsgenerated by the one or more RF apparatuses 102, internal data regardingthe operation of the one or more RF apparatuses 102, a combinationthereof, and/or the like.

In various embodiments, the one or more testers 120 can share thecollected data directly with the one or more machine learning engines118 as performance evaluation data. In some embodiments, the one or moretesters 120 can execute one or more data processing techniques to renderthe collected outputs as evaluation data, sharable with the one or moremachine learning engines 118 in accordance with various embodimentsdescribed further herein. For instance, the one or more testers 120 canstructure and/or format the one or more outputs into performanceevaluation data that can be readily analyzed by the one or more machinelearning engines 118.

The one or more data repositories 124 can comprise historic dataregarding past: operations of the one or more RF apparatuses;determinations by the one or more tuners 103 (e.g., test configurationsettings and/or optimal configuration settings); parameters and/or tasksettings defined by the one or more input/output devices 106; acombination thereof, and/or the like. For example, the one or more datarepositories 124 can store logs (e.g., tables, charts, graphs, and/orthe like) of previously utilized configuration settings and associate RFapparatus 102 performance evaluation data. Additionally, the one or moredata repositories 124 can comprise a library of transfer learning models122. As shown in FIG. 1 , the one or more data repositories 124 can beoperably coupled to, and thereby share data with, the one or more:tuners 103, input/output devices 106, testers 120, and/or RF apparatuses102.

In various embodiments, the one or more data repositories 124 caninclude one or more transfer learning models, which can be machinelearning models pre-trained with regards to one or more previous machinelearning tasks. For example, the one or more transfer learning modelscan include pre-trained machine learning models trained on datacharacterizing respective types of RF apparatuses 102. In anotherexample, the one or more transfer learning models can includepre-trained machine learning models trained to optimize theconfiguration settings of other types of devices (e.g., devices otherthan the one or more RF apparatuses 102).

FIG. 2 illustrates a diagram of the example, non-limiting tuner 103further comprising transfer learning engine 204 and/or one or moremachine learning models 206 in accordance with various embodimentsdescribed herein. As shown in FIG. 2 , the transfer learning engine 204and/or machine learning models 206 can be comprised within the computerreadable storage media 110 of the one or more tuners 103. However,embodiments in which the transfer learning engine 204 and/or machinelearning models 206 are remotely accessed by the one or more tuners 103(e.g., via the one or more networks 104) are also envisaged.

In one or more embodiments, the machine learning engine 118 can generateand/or train a new machine learning model 206 for each tuning operationperformed by the one or more tuners 103. Alternatively, the machinelearning engine 118 can select a machine learning model 206 from a modelcandidate list 208 comprising a plurality of previously generatedmachine learning models 206 that can be utilized, further trained,and/or adjusted to execute the given tuning operation. For example, themodel candidate list 208 can be populated with one or more machinelearning models 206 previously employed by the one or more tuners 103 totune a respective RF apparatus 102 and/or a respective type of RFapparatus 102 (e.g., another RF apparatus 102 from the same productline).

Additionally, one or more of the machine learning models 206 included inthe model candidate list 208 can be transfer learning models identifiedby the transfer learning engine 204. For example, one or more of themachine learning models 206 can be transfer learning models previouslytrained during one or more tuning operations of an RF apparatuses 102other than the respective RF apparatus 102 currently subject to tuning.In another example, one or more of the machine learning models 206 canbe transfer learning models trained on RF apparatus 102 performance datafor one or more other machine learning tasks (e.g., other than the oneor more tuning operations described herein). In a further example, oneor more of the machine learning models 206 can be transfer learningmodels trained for tuners other than the one or more RF apparatuses 102.

In various embodiments, the transfer learning engine 204 can identifyone or more transfer learning models for inclusion in the modelcandidate list 208 based on one or more similarities in, for example,the parameter space of the model and the variable parameters influencedby the configuration settings of the RF apparatuses 102. For instance,the transfer learning engine 204 can compare the variable componentsincluded in the one or more RF apparatuses 102 selected for tuning withthe internal components of one or more devices analyzed by the transferlearning model. Where the one or more RF apparatuses 102 and otherdevices share the same internal components, potential settingsconfigurations, operational constraints, and/or perform similarfunctions; the transfer learning engine 204 may populate the modelcandidate list 208 with the associate transfer learning model.

In various embodiments, the one or more machine learning models 206 canbe, for example, multi-layer ANN models. For example, the one or moremachine learning models 206 can be ANN models comprising interconnectedinput layers, a plurality of hidden layers, and/or output layers. Theinput layers can regard parameter values controllable via variablecomponents of the one or more RF apparatuses 102. The output layers canregard evaluation data characterizing output signals and/or operationfeatures of the one or more RF apparatuses 102. Further, the pluralityof hidden layers can be interconnected between the input and outputlayers via a plurality of nodes and/or edges (e.g., with associateweight values). For instance, the hidden layers can be fully-connectedlayers having multiple nodes. In various embodiment, the one or moremachine learning models 206 can be regression models that map tuningparameters to predicted RF apparatus 102 performance results. Forexample, the one or more machine learning models 206 can be responsesurface models, such as Gaussian process (“GP”) models and/or randomforest models, that can be utilized by the machine learning engine 118via one or more sequential model-based optimization algorithms inaccordance with one or more embodiments described herein. For instance,the one or more machine learning models 206 can represent probabilisticrelationships between configuration settings and predicted performancedata, where each mapped relationship can have an associate probabilityindicative of the model's confidence in the accuracy of the predictedresult.

FIG. 3 illustrates a diagram of an example, non-limiting tuningoperation 300 that can be executed by the system 100 in accordance withone or more embodiments described herein. At 302, the one or moreinput/output devices 106 can share task data 304 with the one or moretuners 103. Additionally, or alternatively, one or more elements of thetask data 304 can be shared with the one or more tuners 103 by the oneor more data repositories 124. In accordance with various embodimentsdescribed herein, the task data 304 can define, for example: operationalconstraints of the one or more RF apparatuses 102, safety constraintsassociated with operation of the one or more RF apparatuses 102,optimization threshold values, operation parameters, RF apparatus 102specification data, task objectives, tuning objectives, a combinationthereof, and/or the like. For instance, the task data 304 can include,but are not limited to: attributes and/or metadata characterizing theone or more RF apparatuses 102 (e.g., RF apparatus 102 serial numbers,part numbers, and/or the like); permissible parameter ranges; associateprobability distributions (e.g., delineating uniform and/or normaldistributions); measurement losses targeted for optimization (e.g., tobe maximized or minimized), including multiple measurement losses to beoptimized; prioritizations associated with one or more objectivesdefined by measurement losses targeted for optimization (e.g., includinga Pareto front); a combination thereof; and/or the like. Additionally,the task data 304 can include one or more external parameters that theone or more RF apparatuses 102 may be exposed to during operation (e.g.,a defined temperature range and/or signal interference).

Based on the task data 304, the one or more tuners 103 can initialize atuning operation 300 by selecting one or more machine learning models206 for tuning the one or more RF apparatuses 102. For example, the oneor more tuners 103 can utilize a machine learning engine 118 to generateand/or train a machine learning model 206 based on the task data 304 inaccordance with various embodiments described herein. In anotherexample, the one or more tuners 103 can utilize a transfer learningengine 204 to select a pre-trained machine learning model 206 based onthe task data 304 in accordance with various embodiments describedherein.

At 306, the one or more tuners 103 can share one or more configurationsettings 308 with the one or more testers 120 in accordance with variousembodiments described herein. Additionally, at 306 the one or moretuners 103 can share data comprised within the task data 304 with theone or more testers 120. In accordance with various embodimentsdescribed herein, the machine learning engine 118 can utilize the one ormore machine learning models 206 to generate the one or moreconfiguration settings 308. For example, the one or more tuners 103 canengage a first iteration of the tuning operation 300 by sending initialconfiguration settings 308 to the testers 120. In one or moreembodiments, the one or more tuners 103 can generate the initialconfiguration settings 308 based on a random selection process and/orbased on one or more initialization preferences defined in the task data304. In subsequent iterations of the tuning operation 300, the one ormore tuners 103 can generate configuration settings 308 using a machinelearning model 206 that is updated based on previously testedconfiguration settings 308 and/or evaluation data. In accordance withvarious embodiments described herein, the configuration settings 308 candelineate how to set one or more of the variable components of the oneor more RF apparatuses 102; thereby, the configuration settings 308 canmodulate one or more operational parameters of the one or more RFapparatuses 102 and/or parameters characterizing one or more outputs ofthe RF apparatuses 102.

At 310, the one or more testers 120 can set the configuration controls312 of the one or more RF apparatuses 102 in accordance with the lastreceived configuration settings 308. As described herein, theconfiguration controls 312 can include adjustments to one or morevariable components of the RF apparatus 102 to meet the configurationsettings 308 for the given iteration of the tuning operation 300.Additionally, at 314, the one or more testers 120 can provide the one ormore RF apparatuses 102 with one or more task inputs 316. As describedherein, the one or more task inputs 316 can define one or more tasks tobe executed by the one or more RF apparatuses 102 and/or can includeinput data to be analyzed, controlled, and/or otherwise augmented by theone or more RF apparatuses 102. In accordance with various embodimentsdescribed herein, the one or more task inputs 316 can include, forexample: data to be analyzed by the one or more RF apparatuses 102during the test operation; task objectives to be completed by the one ormore RF apparatuses 102 during the test operation; modes of operation tobe implemented by the one or more RF apparatuses 102 during the testoperation; a combination thereof, and/or the like.

Based on the one or more task inputs 316 and/or configuration controls312 defined by the one or more testers 120, the one or more RFapparatuses 102 can execute a test operation and generate one or moretask outputs 318, such as one or more output signals. At 320, the one ormore RF apparatuses 102 can share the one or more task outputs 318 withthe one or more testers 120 to evaluate the performance of the one ormore RF apparatuses 102 during the test operation. Optionally, at 322the one or more testers 120 can further collect internal data 324characterizing the internal operations of the one or more RF apparatuses102. For instance, the internal data 324 can include one or moremeasurements of various components of the one or more RF apparatuses 102during the test operation. Example measurements that can be comprised inthe internal data 324 include, but are not limited to: temperaturemeasurements (e.g., the temperature of respective RF apparatus 102components and/or regions of the RF apparatus 102), time measurements(e.g., how long respective RF apparatus 102 components are active),vibration measurements, voltage measurements, power measurements,spectral power measurements, humidity measurements, operating hoursmeasurements and/or tracking, a combination thereof, and/or the like.

In various embodiments, the internal data 324 can also includeidentification information (e.g., name, serial number, model number, acombination thereof, and/or the like) that identifies: the particular RFapparatus 102 associated with a given set of task outputs 416; and/orparticular components of the RF apparatus 102. For example, where atester 120 controls the test operations of multiple RF apparatuses 102,the internal data 324 can be utilized to correlate task outputs 318 tothe respective RF apparatus 102 that generated the task outputs 318. Inone or more embodiments, the one or more testers 120 can collect theinternal data 324 while the test operation is being executed by the oneor more RF apparatuses 102. Alternatively, the one or more testers 120can collect the internal data 324 after completion of the one or moretest operations.

In one or more embodiments, the one or more testers 120 can controlmultiple test operations on the one or more RF apparatuses 102 with thesame configuration controls 312 and/or task inputs 316. For instance,the one or more testers 120 can control multiple runs of the testoperation and collect task outputs 318 and/or internal data 324 witheach run. Thus, a given iteration of the tuning operation 300 cancomprise multiple executions of the same test operation (e.g., utilizingthe same configuration controls 312). By executing multiple runs of thetest operation, the one or more testers 120 can improve the accuracy ofthe performance evaluation data 326 associated with the given iterationof the tuning operation 300.

In various embodiments, the one or more testers 120 can perform one ormore data processing techniques to analyze the task outputs 318 and/orinternal data 324 and generate the performance evaluation data 326.Example data processing techniques that can be employed by the one ormore testers 120 can include, but are not limited to: data aggregation,dataset pruning, data mining, data imputation, data standardization,data validation, data transformation, a combination thereof, and/or thelike. For example, the one or more testers 120 can extract: one or moreparameters of the output signal of the one or more RF apparatuses 102,and/or one or more operating parameters of the one or more RFapparatuses 102. Further, the one or more extracted parameters canconstitute the performance evaluation data 326. Example parameters thatthe one or more testers 120 can extract from the task outputs 318 (e.g.,output signals) can include, but are not limited to: rise time, risetime slope, fall time, fall time slope, pulse width, pk-pk amplitude,mean amplitude displacement from zero, phase, band flatness, in-bandspectral emission, out-of-band spectral emissions, error vectormagnitude, a combination thereof, and/or the like. Example operatingparameters that the one or more testers 120 can extract from the taskoutputs 318 and/or internal data 324 can include, but are not limitedto: filter coefficient, output power, a combination thereof, and/or thelike.

At 330, the one or more testers 120 can share the performance evaluationdata 326 with the one or more tuners 103, which can analyze theperformance evaluation data 326 to determine one or more performancemetrics that characterize the quality of performance achieved by the oneor more RF apparatuses 102 during the first iteration of the tuningoperation 300. For example, the machine learning engine 118 can comparethe performance evaluation data 326 to target performance datacharacterizing a desired (e.g., optimal) operation of the one or more RFapparatuses 102 given the same task inputs 316. In one or moreembodiments, the target performance data can be defined by the one ormore input/output devices 106. For instance, the target performance datacan be included in the task data 304 shared with the one or more tuners103 at 302. In a further instance, the target performance data can bestored in the computer readable storage media 110 of the tuners 103 orretrieved from the one or more data repositories 124.

In various embodiments, the one or more tuners 103 (e.g., via machinelearning engine 118) can execute a loss function algorithm, such as acorrelation-based (e.g., a cross-correlation coefficient of two timeseries measurements from the performance evaluation data 326) and/orerror-based loss function algorithm, to generate the performance metric(e.g., a loss value) based on the performance evaluation data 326 andthe target performance data. Example loss function algorithms that canbe executed by the one or more tuners 103 (e.g., via machine learningengine 118) to determine the performance metric 328 can include, but arenot limited to: a mean square error loss algorithm, a mean absoluteerror loss algorithm, a Huber loss algorithm, a log-cosh loss algorithm,a quantile loss algorithm, a combination thereof, and/or the like.

In one or more embodiments, the one or more one or more tuners 103(e.g., via machine learning engine 118) can compare the performancemetric to one or more optimization thresholds to determine whether theone or more RF apparatuses 102 are sufficiently tuned by theconfiguration settings 308 used in the given iteration of the tuningoperation 300. For instance, where the performance metric is a lossvalue, the one or more tuners 103 can determine that an RF apparatus 102is sufficiently tuned (e.g., performing to a desired optimization level)when the performance metric is less than a defined loss value (e.g.,thereby indicating a desired amount of similarity between theperformance evaluation data 326 and the target performance data). In oneor more embodiments, the bounds of the optimization threshold candefined by the one or more input/output devices 106. For instance, theoptimization threshold (e.g., maximum loss value) can be included in thetask data 304 shared with the one or more tuners 103 at 302. In afurther instance, the optimization threshold can be stored in thecomputer readable storage media 110 of the tuners 103 or retrieved fromthe one or more data repositories 124.

In some embodiments, the tuning operation 300 can repeat features306-330 within a time budget (e.g., defined by the task data 304), wherethe tuning operation 300 can comprise the maximum number of iterationsthat can be performed within the constraints of the time budget; therebygenerating a pool of test configuration settings 308 from which theoptimal configuration setting 308 can be chosen. In one or moreembodiments, the tuning operation 300 can repeat features 306-330 untilthe resulting performance evaluation data 326 meets at least the minimumoptimization standards defined by the one or more optimizationthresholds or until the time budget is exhausted (e.g., whichever eventoccurs first).

For example, where the one or more tuners 103 determine that the one ormore RF apparatuses 102 are sufficiently tuned, the tuning operation 300can end. Further, the one or more tuners 103 can utilize the performanceevaluation data 326 and/or performance metric 328 to train and/orinitialize one or more machine learning models 206 for future tuningoperations 300. Where the one or more tuners 103 determine that the oneor more RF apparatuses 102 need additional tuning (e.g., the testconfiguration settings 308 did not result in an optimal performance),the one or more tuners 103 can update the one or more machine learningmodels 206 based on the performance evaluation data 326 and/orperformance metric 328 and generate one or more new configurationsettings 308 for a subsequent iteration of the tuning operation 300. Forexample, the subsequent iteration of the tuning operation 300 can repeatfeatures 306-330 utilizing the newly generated configuration settings308. Thus, the tuning operation 300 can comprise multiple iterations;however, the one or more machine learning engines 118 of the tuners 103can implement one or more Gaussian processing and/or Bayesianoptimization techniques to minimize the number of iterations.

FIG. 4 illustrates a diagram of an example, non-limiting embodiment ofthe machine learning engine 118 further comprising training component402, initialization component 404, evaluation component 406, historycomponent 408, model update component 410, and/or candidate component412. In various embodiments, the associate components of the machinelearning engine 118 can be computer executable components 114 stored inthe or more computer readable storage media 110 of the one or moretuners 103, or stored elsewhere in the system 100 and remotely accessedby the machine learning engine 118. As shown in FIG. 4 , the machinelearning engine 118 can execute a training stage 400 for training and/orfitting one or more machine learning models 206 and/or an inferencestage 401 for identifying configuration settings 308 to be tested in oneor more iterations of a tuning operation (e.g., example tuning operation300). In various embodiments, the machine learning engine 118 canexecute a Bayesian optimization algorithm to perform one or moreiterations of the training stage 400 (e.g., comprising fitting themachine learning models 206 to training datasets 414 and/or performanceevaluation data 326) and the inference stage 401 (e.g., where themachine learning engine 118 is configured to, based on the machinelearning model 206, search the parameter space for new configurationsettings 308 to investigate based on, for example, expected performanceimprovement).

During the training stage 400, the training component 402 can train theone or more machine learning models 206 on one or more training datasets414. The one or more training datasets 414 can be stored in the computerreadable storage media 110. Alternatively, the one or more trainingdatasets 414 can be stored off the one or more tuners 103 and remotelyaccessed by the training component 402. For example, the trainingcomponent 402 can retrieve the one or more training datasets 414 fromthe one or more input/output devices 106 and/or data repositories 124.In various embodiments, the one or more training datasets 414 caninclude historic data regarding, but not limited to: RF apparatus 102performance data (e.g., including performance evaluation data 326 fromprevious, and/or other, tuning operations); previous operations of theone or more RF apparatuses 102; and/or previous determinations by theone or more tuners 103. In one or more embodiments, the one or moretraining datasets 414 can also include synthetic data relating to RFapparatus 102 performance, such as, data obtained from EM modelingsoftware. Examples of EM modeling software tools that can provide RFapparatus 102 performance parameters are a SONNET SUITES tool availablefrom Sonnet Software, Inc. and ANSYS HFSS design tool available fromAnsys, Inc. Additionally, the one or more training datasets 414 caninclude labelled and non-labelled data.

In various embodiments, the training component 402 can executesupervised learning, unsupervised learning, and/or reinforcementlearning techniques to train the one or more machine learning models 206on the training datasets 414 and/or performance evaluation data 326(e.g., thereby generating one or more trained machine learning models206 a), which can be utilized by the machine learning engine 118 toexecute one or more tuning operations described herein. For example, thetraining stage 400 can involve changing weights associated with nodes inlayers of the one or more machine learning models 206 over multipleiterations until an expected output is obtained for particular traininginput data from the training dataset 414. One or more learningalgorithms can be used to train layers of the one or more machinelearning models 206. For example, a gradient descent algorithm andbackpropagation algorithm can be used in tandem when the one or moremachine learning models 206 are deep multi-layer ANN models. In variousembodiments, supervised and/or unsupervised learning can be used tochange weights to minimize a loss function. Reinforcement learning canbe used to change weights to maximize a reward function. In furtherexamples, activation functions, such as a sigmoid function, may also beused especially after layers with weights. Data fitting orregularization techniques to achieve a balanced ANN model and avoidundesired overfitting or underfitting can also be used. Additionally,further optimizations may be employed to improve training, such as,expanding the training dataset 414 with augmentation, increasingtraining time or the depth (or width) of the machine learning model 206,adding regularization, or increasing hyperparameter tuning as would beapparent to person skilled in the art given this description.

During the inference stage 401, the machine learning engine 118 canutilize the one or more machine learning models 206 (e.g., trainedmachine learning models 206 a) to execute a Bayesian optimizationalgorithm to identify configuration settings 308 to be tested and/orevaluated during the tuning operation. In various embodiments, theinitialization component 404 can initialize the Bayesian optimization byselecting one or more initial configuration settings 308 from apermissible range, which can be defined by the task data 304 and/or byhistoric data (e.g., stored in the one or more data repositories 124).For instance, the initialization component 404 can randomly choose theinitial configuration settings 308. In another instance, theinitialization component 404 can choose the initial configurationsettings 308 based on one or more past tuning operations performed onthe one or more RF apparatuses 102. In a further instance, theinitialization component 404 can choose the initial configurationsettings 308 based on one or more tuning operations performed on otherRF apparatuses 102. In a still further instance, the initializationcomponent 404 can employ a pre-trained transfer learning model to choosethe one or more initial configuration settings 308.

In accordance with various embodiments described herein, the one or moretesters 120 can execute one or more test operations on the one or moreRF apparatuses 102 in accordance with the initial configuration settings308 to generate performance evaluation data 326. The evaluationcomponent 406 can compare the performance evaluation data 326 to thepredicted performance data (e.g., target performance data) via a cost orloss function algorithm to generate the performance metric. Further, thehistory component 408 can store the performance evaluation data 326,performance metric, and/or one or more associate model hyperparametersin a historic data log. For example, the history component 408 canupdate the training dataset 414 with the performance evaluation data326, performance metric, and/or one or more associate modelhyperparameters. In another example, the history component 408 can storethe historic data log in the one or more data repositories 124.

Where the performance metric is outside the bounds of one or moredefined optimization thresholds (e.g., defined via the task data 304),the model update component 410 can update the machine learning model 206(e.g., which can be surrogate model, such as a GP model or a RandomForest model, that provides a probabilistic representation of therelationship between configuration settings 308 and RF apparatus 102performance in accordance with various embodiments described herein).For example, the model update component 410 can fit the machine learningmodel 206 to the historic data log. For instance, the model updatecomponent 410 can tune one or more hyperparameters of the machinelearning model 206 based on the historic data log (e.g., the resultsassociated with tested configuration settings 308 from the previousmodel configuration). Thereby, the updated machine learning model 206can predict the RF apparatus 102 performance associated with potentialconfiguration settings 308 with greater accuracy than previouslyexhibited.

Subsequently, the candidate component 412 can apply one or moreacquisition functions to the updated machine learning model 206 andselect a new configuration setting 308 for testing. In variousembodiments, the one or more acquisition functions can analyze thepossible configuration settings 308 represented by the parameter spacealong with the associate probability values. For instance, the candidatecomponent 412 can apply an expected improvement acquisition function toidentify one or more configuration settings 308 predicted to provide themaximum improvement to performance based on the newly fitted machinelearning model 206. In one or more embodiments, the candidate component412 can execute one or more acquisition functions that balance betweenexploration and exploitation objectives (e.g., which can be defined inthe task data 304). The configuration settings 308 identified by thecandidate component 412 can then be tested by the one or more testers120 and further performance evaluation data 326 can be analyzed by themachine learning engine 118. In one or more embodiments, the machinelearning engine 118 can repeat the features of the training stage 400and/or inference stage 401 a minimum number of times to achieve aperformance metric that meets the bounds of the defined optimizationthreshold. In one or more embodiments, the machine learning engine 118can repeat the features of the training stage 400 and/or the inferencestage 401 as many times that is capable within a defined time budget(e.g., where at the end of the time budget the tested configurationsetting 308 associated with the best performance metric can be used totune the one or more RF apparatuses 102).

FIG. 5 illustrates a flow diagram of an example, non-limitingcomputer-implemented method 500 that can be implemented by the system100 in accordance with one or more embodiments described herein to tuneone or more RF apparatuses 102 for optimal performance.

At 502, the computer-implemented method 500 can comprise generating oneor more initial configuration settings 308. For example, the machinelearning engine 118 can randomly select the initial configurationsettings 308 from the combinatorial parameter space of a machinelearning model 206. At 504, the computer-implemented method 500 cancomprise executing one or more test operations on one or more RFapparatuses 102 in accordance with the configuration settings 308 (e.g.,in accordance with the initial configuration settings 308). For example,the one or more testers 120 can set one or more configuration controls312 to adjust one or more variable components of the RF apparatus 102and meet provided the configuration settings 308. Additionally, the oneor more testers 120 can control operation of the RF apparatus 102 inaccordance with one or more operational and/or safety constraints (e.g.,which can be defined via the task data 304 and implemented via the oneor more task inputs 316).

As a result of the test operations, performance evaluation data 326 canbe collected by the one or more testers 120, where the performanceevaluation data 326 can characterize the one or more test operationsperformed at 504 in accordance with various embodiments describedherein. At 506, the computer-implemented method 500 can compriseevaluating the performance evaluation data 326 to determine aperformance metric. For example, the one or more testers 120 can executeone or more loss function algorithms to compare the performanceevaluation data 326 to target performance data, where the performancemetric can be can be the loss value.

At 508, the computer-implemented method 500 can comprise determiningwhether the performance metric meets the bounds of an optimizationthreshold (e.g., defined via the one or more input/output devices 106and/or included in the task data 304). For example, the one or moretuners 103 can compare the performance metric to one or more definedthreshold values (e.g., defined loss value ranges).

In response to determining that the performance metric is outside thebounds of the optimization threshold, the computer-implemented method500 can proceed to 510, where one or more new configuration settings 308can be generated. For example, the machine learning engine 118 canupdate the one or more machine learning models 206 based on theperformance evaluation data 326 and choose one or more new configurationsettings 308 that are predicted to render the maximum expectedimprovement. For instance, at 510 the machine learning engine 118 cantune one or more hyperparameters of the machine learning model 206 basedon the performance evaluation data 326 and/or performance metric.Further, at 510 the machine learning engine can apply one or moreacquisition functions to the tuned machine learning model 206 togenerate new configuration settings 308 for testing. Additionally, themachine learning engine 118 can consider one or more confidence valuesassociated with the potential configuration settings 308 in selectingthe new configuration settings 308 at 510. Subsequently, thecomputer-implemented method 500 can repeat features 504-508 to analyzethe effects of the new configuration settings 308 on the performance ofthe one or more RF apparatuses 102.

In response to determining that the performance metric is within thebounds of the optimization threshold, the computer-implemented method500 can proceed to 512, where the configuration settings 308 employedduring the latest test operation can be identified as the optimalconfiguration settings 308 for the one or more RF apparatuses 102. Forexample, the bound of the optimization threshold can be less than orequal to a defined loss value, where the one or more tuners 103 candetermine that the one or more RF apparatuses 102 are sufficiently tunedwhen the performance metric is less than or equal to the defined lossvalue.

FIG. 6 illustrates a flow diagram of another example, non-limitingcomputer-implemented method 600 that can be implemented by the system100 in accordance with one or more embodiments described herein. At 602,the computer-implemented method 600 can comprise collecting task data304 that can characterize one or more RF apparatuses 102 and/oroperational constraints. For instance, the task data 304 can be enteredinto the system 100 via the one or more input/output devices 106 andreceived by the one or more tuners 103, as exemplified by tuningoperation 300. The task data 304 collected at 602 can includeinformation identifying the particular RF apparatus 102 subject totuning and/or the location of the RF apparatus 102 within one or morenetworks 104 (e.g., within a communications and/or data network, such asa satellite communications network). For instance, the task data 304collected at 602 can include a model number, serial number, IP address,and/or network address of the one or more RF apparatuses 102. In one ormore embodiments, the one or more tuners 103 can retrieve further dataregarding the operating specifications of the one or more RF apparatuses102 from one or more data repositories 124 based on the identityinformation provided in the task data 304. For example, the operatingspecifications can delineate the type and/or number of variablecomponents included in the one or more RF apparatuses 102 and/or thetype and/or number of parameters that can be controlled by the variablecomponents.

Additionally, the task data 304 can include operational constraints ofthe one or more RF apparatuses 102, such as permissible and/orimpermissible parameter ranges. In various embodiments, the one or moretuners 103 can utilize the operational constraints to ensure thatgenerated configuration settings 308 result in safe operation of the oneor more RF apparatuses 102. For example, configuration settings 308predicted to result in task outputs 318 and/or internal data 324 thatare outside the defined operational constraints can be removed from thecandidate pool of potential test configuration settings 308. Forinstance, the one or more operational constraints can delineate amaximum peak power value, where the one or more tuners 103 can excludepotential configuration settings 308 that are predicted to result in apeak power value that exceeds the maximum peak power value. In variousembodiments, the one or more operational constraints can be defined toprotect the safety of one or more users and/or of the integrity of theRF apparatuses 102.

In accordance with various embodiments described herein, the task data304 collected at 602 can further characterize one or more testoperations to be performed by the one or more tuners 103, define one ormore optimization objectives used to tune the one or more RF apparatuses102 (e.g., the type of optimization and/or evaluation algorithm, such asthe type of loss function algorithm), define one or more optimizationthresholds, define one or more computational cost budgets (e.g., a timebudget), a combination thereof, and/or the like.

At 604, the computer-implemented method 600 can optionally initiate(e.g., via machine learning engine 118) a warm start operation to selecta machine learning model 206 for a tuning operation (e.g., in accordancewith example tuning operation 300). The warm start operation cancomprise, for example, a transfer learning algorithm (e.g., executed viathe transfer learning engine 204 in accordance with various embodimentsdescribed herein) that selects a transfer learning model to facilitatethe tuning operation. For example, the transfer learning model can be amachine learning model 206 that was previously trained on one or moreother machine learning tasks. For instance, the transfer learning modelcan be a machine learning model 206 previously trained on a tuningoperation for one or more other RF apparatuses 102 of the same typeand/or model of the RF apparatus 102 currently subject to tuning (e.g.,trained on tuning operations for RF apparatuses 102 of the same productline). In another instance, the transfer learning model can be a machinelearning model 206 previously trained on a tuning operation for one ormore other RF apparatuses 102 that share one or more manufacturingsimilarities with the RF apparatus 102 currently subject to tuning. In afurther instance, the transfer learning model can be a machine learningmodel 206 previously trained on a tuning operation for one or more otherRF apparatuses 102 having one or more of the same variable components asthe RF apparatus 102 currently subject to tuning. By employing the warmstart operation, when a new RF apparatus 102 (e.g., an RF apparatus 102of a newly developed product line) is subject to tuning, the knowledgegained (e.g., lessons learned) from previous tuning operations ofsimilar RF apparatuses 102 can be utilized to initialize the subjecttuning operation (e.g., can be utilized to tailor the combinatorialparameter space) and/or improve the selection of configuration settings308 (e.g., the gained knowledge can improve the accuracy of expectedimprovement determinations).

At 606, the computer-implemented method 600 can comprise employing(e.g., via machine learning engine 118) the one or more selected machinelearning models 206 to generate one or more initial configurationsettings 308 based on the collected task data 304. For example, themachine learning engine 118 can select (e.g., through a randomizedoperation) the one or more initial configuration settings 308 based onone or more permissible ranges defined by the task data 304. At 608, thecomputer-implemented method 600 can comprise executing (e.g., viatesters 120) one or more test operations on the one or more RFapparatuses 102 in accordance with the configuration settings 308. Forexample, the one or more testers 120 can control one or more task inputs316 and/or configuration controls 312 in accordance with variousembodiments described herein.

At 610, the computer-implemented method 600 can comprise collecting(e.g., via testers 120) one or more task outputs 318 and/or internaldata 324 from the one or more RF apparatus 102, which characterize theone or more test operations performed at 608. For example, the one ormore task outputs 318 can include one or more output signals generatedby the one or more RF apparatuses 102 during the test operations, and/orthe internal data 324 can include operation measurements of one or morecomponents of the one or more RF apparatuses. At 612, thecomputer-implemented method 600 can comprise generating (e.g., viatesters 120) performance evaluation data 326 from the collected taskoutputs 318 and/or internal data 324. For example, the one or moretesters 120 can extract one or more parameters characterizing thefeatures of one or more output signals generated by the one or more RFapparatuses 102 in accordance with various embodiments described herein.In another example, the one or more testers 120 can extract one or moreoperational parameters characterizing the operating conditions exhibitedby one or more components of the one or more RF apparatuses inaccordance with various embodiments described herein.

At 614, the computer-implemented method 600 can comprise evaluating(e.g., via machine learning engine 118) the performance evaluation data326 to determine a performance metric. For example, the machine learningengine 118 can evaluate the performance evaluation data 326 inaccordance with one or more optimization objectives defined by the taskdata 304. For instance, the machine learning engine 118 can execute oneor more loss functions to compare the performance evaluation data 326 toa target performance in accordance with various embodiments describedherein.

At 616, the computer-implemented method 600 can comprise determiningwhether the performance metric meets the bounds of an optimizationthreshold (e.g., defined via the one or more input/output devices 106and/or included in the task data 304). For example, the one or moretuners 103 can compare the performance metric to one or more definedthreshold values (e.g., defined loss value ranges).

In response to determining that the performance metric is outside thebounds of the optimization threshold, the computer-implemented method600 can proceed to 618, where one or more new configuration settings 308can be generated. For example, the machine learning engine 118 canupdate the one or more machine learning models 206 based on theperformance evaluation data 326 and choose one or more new configurationsettings 308 that are predicted to render the maximum expectedimprovement. Additionally, the machine learning engine 118 can considerone or more confidence values associated with the potentialconfiguration settings 308 in selecting the new configuration settings308 at 618. Subsequently, the computer-implemented method 600 can repeatfeatures 608-616 to analyze the effects of the new configurationsettings 308 on the performance of the one or more RF apparatuses 102.

In response to determining that the performance metric is within thebounds of the optimization threshold, the computer-implemented method600 can proceed to 620, where the configuration settings 308 employedduring the latest test operation can be identified as the optimalconfiguration settings 308 for the one or more RF apparatuses 102. At622, the computer-implemented method 600 can comprise storing the one ormore optimal configuration settings 308 in the one or more datarepositories 124 along with historic optimal configuration settings 308(e.g., retrieved from other tuning operations). At 624, thecomputer-implemented method 600 can comprise training one or moremachine learning models 206 using the optimal configuration settings 308from the one or more data repositories 124.

FIGS. 7A-7B illustrate diagrams of the example, non-limiting system 100in which the one or more testers 120 and/or tuners 103 can be comprisedwithin the one or more RF apparatuses 102 (e.g., rather than accessingthe one or more RF apparatuses 102 remotely) in accordance with one ormore embodiments described herein. For example, FIGS. 7A-B depictsexample embodiments in which the one or more RF apparatuses 102 furthercomprise the one or more testers 120, which can then communicate withthe one or more tuners 103 (e.g., via a wireless connection across theone or more networks 104, such as a cloud computing environment). Forinstance, the one or more testers 120 can be computer executablecomponents 114 embedded and/or otherwise stored on the one or more RFapparatuses 102. Additionally, the one or more testers 120 can furthercomprise, and/or be operably coupled to, one or more sensors 704 thatcan measure and/or collect the one or more task outputs 318 and/orinternal data 324.

In the example embodiment shown in FIG. 7A, the one or more on-boardtesters 120 can communicate, and/or share data, with the one or moreremote tuners 103 via the one or more networks 104 in accordance withthe various embodiments described herein. In the example embodimentshown in FIG. 7B, the one or more RF apparatuses 102 can also compriseone or more on-board tuners 103, which can communicate, and/or sharedata, with the one or more testers 120 via, for example, a directelectrical connection and/or local wireless connection (e.g., via theone or more networks 104). In one or more embodiments, the on-boardtuner 103 can comprise the machine learning engine 118, transferlearning engine 204, and/or machine learning models 206. In someembodiments, the on-board tuner 103 can comprise the machine learningengine 118 and/or the transfer learning engine 204, while one or more ofthe machine learning models 206 can be remotely accessed from the one ormore data repositories 124.

FIG. 8 illustrates a diagram of the example, non-limiting system 100 inwhich multiple RF apparatuses 102 can be tuned by the one or more tuners103 (e.g., tuned simultaneously, concurrently, and/or sequentially) inaccordance with one or more embodiments described herein. As shown inFIG. 8 , the system 100 can comprise multiple RF apparatuses 102 (e.g.,a first RF apparatus 102 a, a second RF apparatus 102 b, and/or one ormore other RF apparatuses 102 n). Each of the RF apparatuses 102 can beoperated by a respective tester 120 (e.g., a first tester 120 a, asecond tester 120 b, and/or one or more other testers 120 n), which canbe on-board testers 120 or remote testers 120. Alternatively, a singletester 120 can operate multiple RF apparatuses 102. Additionally, theone or more testers 120 can communicate with one or more tuners 103. Forexample, multiple testers 120 can communicate with a common tuner 103via the one or more networks 104 (e.g., via a cloud computingenvironment). For example, a single tuner 103 can be tasked withperforming tuning operations on a group of RF apparatuses 102.

In various embodiments, the common tuner 103 can perform one or moretuning operations (e.g., exemplified by tuning operation 300) on thevarious RF apparatuses 102 simultaneously, concurrently, and/orsequentially. Additionally, the tuner 103 can perform the tuningoperation for the second RF apparatus 102 b based on, for example, thetuning operation for the first RF apparatus 102 a and/or the other RFapparatuses 102 n. For instance, an optimal configuration setting 308identified for the first RF apparatus 102 a can serve as the initialconfiguration setting 308 for the second RF apparatus 102 b. In anotherinstance, test configuration settings 308 employed in test operations onthe first RF apparatus 102 a can be avoided in selecting the initialconfiguration settings 308 for the second RF apparatus 102 b. In afurther instance, the performance evaluation data 326 characterizingtest operations on the first RF apparatus 102 a can be used to updateand/or fit a machine learning model 206 employed to tune the second RFapparatus 102 b and/or the other RF apparatuses 102 n. Additionally, thetuner 103 can utilize task data 304 regarding the first RF apparatus 102a to replace missing information in the task data 304 regarding thesecond RF apparatus 102 b and/or other RF apparatuses 102 n.

FIG. 9 illustrates a diagram of example, non-limiting graphs 900, 902that can characterize one or more tuning operations that can beimplemented by the system 100 during a first example use case inaccordance with one or more embodiments described herein. In the firstexample use case, an RF apparatus 102 (e.g., an RF amplifier) can beoptimized for use in a time-divisional multiple access (“TDMA”) digitalcommunication network. Wireless network protocols can requireparticipating RF apparatuses 102 to transmit and/or receive wirelesssignals according to standardized performance specifications.

Due to cost complexity and/or manufacturing variations, the RF apparatus102 may fail to meet the standardized specifications after initialassembly. For example, the RF apparatus's 102 performance after initialassembly can be distorted as a result of intrinsic material performancevariation over frequency as well as variations resulting from componentfabrication and/or assembly processes. To account for the variations,the RF apparatus 102 can include a front-end system with adjustableconfiguration controls that can be tuned (e.g., via the one or moretuners 103) to result in a performance that meets network specificationsacross multiple frequencies, configurations, and/or operating conditions(e.g., temperature ranges).

For example, the RF apparatus 102 (e.g., an RF amplifier) can bedesigned with variable gain, which is controlled by adjusting atransistor's bias level. The bias level input can be given apre-distorted pulse shape generated by one or more variable components(e.g., an FPGA or microcontroller). Parameters that define thepre-distorted pulse shape can be modulated via one or more configurationsettings 308 determined by the tuners 103. Thus, the tuning operationperformed by the one or more tuners 103 can determine control signalconfiguration settings 308 (e.g., rise time slope, fall time slope,pulse width, pk-pk amplitude, mean amplitude displacement from zero (“DCoffset”), and/or the like) such that an output signal of the RFapparatus 102 meets the standardized specifications of the TDMA digitalcommunication network. For example, the output signal can becharacterized by performance evaluation data 326 that includes amplitudevariation, rise time, fall time, pulse width, output power, in-bandspectral emissions, out-of-band spectral emissions, error vectormagnitude, a combination thereof, and/or the like. In accordance withvarious embodiments described herein, the one or more tuners 103 canperform one or more tuning operations (e.g., comprising one or moreiterations) via a sequential model-based Bayesian optimization algorithmto tune the configuration settings 308, where the machine learningengine 118 can utilize various single or multiple loss functions (e.g.,cross-correlation and/or mean absolute error algorithms) between targetperformance data and collected performance evaluation data 326.

For instance, graph 900 depicts performance evaluation data 326exhibited by the RF apparatus 102 prior to a tuning operation performedby the one or more tuners 103. As shown in FIG. 9 , the performanceevaluation data 326 can characterize parameters 1, 2, and 3; havingvalues that are adjustable along the associate range 904. Graph 902depicts the performance evaluation data 326 exhibited by the RFapparatus 102 subsequent to the tuning operation performed by the one ormore tuners 103. As shown in graph 902, the tuning operation canoptimize the performance of the RF apparatus 102 to generate an outputsignal that is characteristic of target (e.g., ideal) performance data.

ADDITIONAL EMBODIMENTS

The present disclosure is also directed to the following exemplaryembodiments:

Embodiment 1: A system, comprising: a radio frequency apparatusconfigured to operate based on a plurality of possible configurationsettings to generate an output signal that is characterized by aperformance metric; and a tuner that employs a machine learning enginehaving a training stage and an inference stage, where the inferencestage is configured to, based on a machine learning model, search thepossible configuration settings for a target configuration setting thatresults in the performance metric meeting defined bounds of anoptimization threshold value.

Embodiment 2: The system of embodiment 1, further comprising: a testerthat controls operation of the radio frequency apparatus based on aplurality of test configuration settings identified by the tuner,wherein the target configuration setting is from the plurality of testconfiguration settings.

Embodiment 3: The system of any of embodiments 1 and/or 2, where theradio frequency apparatus is an amplifier, filter, digital signalprocessor, radio frequency integrated circuit, micro-electro-mechanicalsystem filter, and/or monolithic microwave integrated circuit.

Embodiment 4: The system of any of embodiments 1-3, where the pluralityof test configuration settings modulate at least one parameter of theoutput signal or operating parameter of the radio frequency apparatus.

Embodiment 5: The system of embodiment 4, where the at least oneparameter of the output signal includes: amplitude variation, rise time,fall time, pulse width, output power, in-band spectral emissions,out-of-band spectral emissions, error vector magnitude, and/or acombination thereof.

Embodiment 6: The system of embodiment 4, where the at least oneoperating parameter of the radio frequency apparatus includes: filtercoefficient, output power, and/or a combination thereof.

Embodiment 7: The system of any of embodiments 1-4, where theperformance metric is a function of performance evaluation data thatcharacterizes the output signal or the operating parameter of the radiofrequency apparatus.

Embodiment 8: The system of embodiment 7, where the tuner determines theperformance metric by comparing the performance evaluation data to atarget performance dataset.

Embodiment 9: The system of any of embodiments 7 and/or 8, where thetester determines the performance metric by executing a loss functionalgorithm. Also, the defined bounds of the optimization threshold is arange less than or equal to a defined loss value.

Embodiment 10: The system of any of embodiments 7-9, where the lossfunction algorithm is a correlation-based loss function algorithm or anerror-based loss function algorithm.

Embodiment 11: The system of embodiment 1, where the machine learningengine executes a Bayesian optimization algorithm to identify theplurality of test configuration settings based on historic performancemetrics that characterize previous output signals generated by the radiofrequency apparatus in response to operations controlled by the tester.

Embodiment 12: The system of embodiment 1, where the tester is acomputer executable component stored in a computer readable storagemedium comprised within the radio frequency apparatus.

Embodiment 13: The system of embodiment 1, where the tester sends thehistoric performance metrics to the tuner and receives the plurality oftest configuration settings from the tuner via a cloud computingenvironment.

Embodiment 14: The system of embodiment 1, where the machine learningengine comprises computer executable components that include aninitialization component that selects an initial configuration settingfrom the plurality of possible configuration settings; and where thesystem further comprises a tester that controls operation of the radiofrequency apparatus in accordance with the initial configurationsetting.

Embodiment 15: The system of embodiment 14, where the initializationcomponent randomly selects the initial configuration setting.

Embodiment 16: The system of any of embodiments 14 and/or 15, where thecomputer executable components further include a model update componentsthat tunes a hyperparameter of the machine learning model based on theperformance metric that characterizes the output generated from apreviously tested configuration setting.

Embodiment 17: The system of any of embodiments 14-16, where thecomputer executable components further include a candidate componentthat selects a test configuration setting based on the tuned machinelearning model, and wherein the tester further controls the operation ofthe radio frequency apparatus in accordance with the test configurationsetting.

Embodiment 18: The system of any of embodiments 14-17, where the targetconfiguration setting optimizes the radio frequency apparatus for use ina time-divisional multiple access digital communications network.

Embodiment 19: A computer-implemented method for tuning a configurationsetting of a radio frequency apparatus, the computer-implemented methodcomprising: applying a machine learning model to generate a testconfiguration setting for the radio frequency apparatus; generatingperformance evaluation data by operating the radio frequency apparatuswith the test configuration setting; and comparing the performanceevaluation data to a target performance dataset to determine whether thetest configuration setting is an optimal configuration setting for adefined objective.

Embodiment 20: The computer-implemented method of embodiment 19, wherethe machine learning model is a regression model that defines aplurality of probabilistic relationships between parameters of the radiofrequency apparatus that are controllable via the test configurationsetting and predicted performance data.

Embodiment 21: The computer-implemented method of any of embodiments 19and/or 20, where the machine learning model is a Gaussian process modelor a Random Forest model, and where the applying the machine learningmodel is performed by a machine learning engine executing a Bayesianoptimization algorithm.

Embodiment 22: The computer-implemented method of any of embodiments19-21, further comprising: determining that the test configurationsetting is sub-optimal based on a performance metric that characterizesthe performance data being less than the optimization threshold;generating an updated machine learning model by adjusting one or morehyperparameters based on the performance evaluation data and the testconfiguration setting; and executing the updated machine learning modelto generate a second test configuration setting for the radio frequencyapparatus.

Embodiment 23: The computer-implemented method of embodiment 22, furthercomprising generating the performance metric by executing a lossfunction algorithm that compares the performance evaluation data to thetarget performance dataset.

Embodiment 24: The computer-implemented method of embodiment 23, wherethe loss function algorithm is a correlation-based loss functionalgorithm or an error-based loss function algorithm.

Embodiment 25: The computer-implemented method of any of embodiments19-24, where the radio frequency apparatus is an amplifier, filter,digital signal processor, radio frequency integrated circuit,micro-electro-mechanical system filter, or monolithic microwaveintegrated circuit.

Embodiment 26: The computer-implemented method of any of embodiments19-25, where the test configuration setting modulates an output signalor operating parameter of the radio frequency apparatus characterized bythe performance evaluation data.

Embodiment 27: A computer program product for tuning configurationsettings of a radio frequency apparatus, the computer program productcomprising a computer readable storage medium having programinstructions embodied therewith, the program instructions executable byone or more processors to cause the one or more processors to: controlan operation of a radio frequency apparatus using an initialconfiguration setting; update a machine learning model based onperformance evaluation data characterizing the operation of the radiofrequency apparatus; and determine a test configuration setting for theradio frequency apparatus based on a prediction generated by the machinelearning model regarding a second operation of the radio frequencyapparatus using the test configuration setting.

Embodiment 28: The computer program product of embodiment 27, where themachine learning model is a Gaussian process model or a Random Forestsmodel.

Embodiment 29: The computer program product of any of embodiments 27and/or 28, where wherein the program instructions further cause the oneor more processors to: execute the second operation of the radiofrequency apparatus using the test configuration setting; collectadditional performance evaluation data charactering the second operationof the radio frequency apparatus; and compare the additional performanceevaluation data to an optimization threshold to determine whether thetest configuration setting is an optimal configuration setting.

Embodiment 30: The computer program product of any of embodiments 27-29,where the program instructions further cause the one or more processorsto: select the test configuration setting based on one or moreperformance constraints regarding the radio frequency apparatus.

Embodiment 31: The computer program product of any of embodiments 27-30,where the program instructions further cause the one or more processorsto: generate the initial configuration setting based on historicperformance evaluation data from a third operation of a second radiofrequency apparatus.

Embodiment 32: The computer program product of any of embodiments 27-30,where the test configuration setting modulates at least one parameter ofan output signal or an operation of the radio frequency apparatus.

Embodiment 33: The computer program product of embodiment 32, where theat least one parameter of the output signal includes: amplitudevariation, rise time, fall time, pulse width, output power, in-bandspectral emissions, out-of-band spectral emissions, error vectormagnitude, or a combination thereof.

Embodiment 34: The computer program product of any of embodiments 32and/or 33, where the at least one parameter of the operation of theradio frequency apparatus includes: filter coefficient, output power,and/or a combination thereof.

Embodiment 35: The computer program product of embodiment 27, where theprogram instructions further cause the one or more processors to executea loss function algorithm to generate a performance metric that comparethe performance evaluation data to a target performance data set.

Embodiment 36: The computer program product of embodiment 27, where themachine learning model is updated by fitting the machine learning modelto historic data that includes the performance evaluation data.

Embodiment 37: The computer program product of embodiment 27, where thetest configuration setting optimizes the radio frequency apparatus foruse in a time-divisional multiple access digital communications network.

In accordance with the various embodiments described herein, one or moreof the computer executable components 114 and/or computer-implementedmethod features described herein can be loaded onto, and/or executed by,a programmable apparatus (e.g., comprising one or more processing units108, such as tuner 103). When executed, the computer executablecomponents 114 and/or computer-implemented method features describedherein can cause the programmable apparatus to implement one or more ofthe various functions and/or operations exemplified in the referencedflow diagrams and/or block diagrams.

In the flow diagrams and/or block diagrams of the Drawings, the variousblocks can represent one or more modules, segments, and/or portions ofcomputer readable instructions for implemented one or more logicalfunctions in accordance with the various embodiments described herein.Additionally, the architecture of the system 100 and/or methodsdescribed herein is not limited to any sequential order illustrated inthe Drawings. For example, two blocks shown in succession can representfunctions that can be performed simultaneously. In a further example,blocks can sometimes be performed in a reverse order from the sequenceshown in the Drawings. Moreover, in one or more embodiments, one or moreof the illustrated blocks can be implemented by special purpose hardwarebased systems.

As used herein, the term “or” is intended to be inclusive, rather thanexclusive. Unless specified otherwise, “X employs A or B” is intended tomean any of the natural incisive permutations. That is, if X employs A;X employs B; or X employs both A and B, the “X employs A or B” issatisfied. Additionally, the articles “a” or “an” should generally beconstrued to mean, unless otherwise specified, “one or more” of therespective noun. As used herein, the terms “example” and/or “exemplary”are utilized to delineate one or more features as an example, instance,or illustration. The subject matter described herein is not limited bysuch examples. Additionally, any aspects, features, and/or designsdescribed herein as an “example” or as “exemplary” are not necessarilyintended to be construed as preferred or advantageous. Likewise, anyaspects, features, and/or designs described herein as an “example” or as“exemplary” is not meant to preclude equivalent embodiments (e.g.,features, structures, and/or methodologies) known to one of ordinaryskill in the art.

Understanding that is not possible to describe each and everyconceivable combination of the various features (e.g., components,products, and/or methods) described herein, one of ordinary skill in theart can recognize that many further combinations and permutations of thevarious embodiments described herein are possible and envisaged.Furthermore, as used herein, the terms “includes,” “has,” “possesses,”and/or the like are intended to be inclusive in a manner similar to theterm “comprising” as interpreted when employed as a transitional word ina claim.

What is claimed is:
 1. A system, comprising: a radio frequency apparatusconfigured to operate based on a plurality of possible configurationsettings to generate an output signal that is characterized by aperformance metric; and a tuner that employs a machine learning enginehaving a training stage and an inference stage, wherein the inferencestage is configured to, based on a machine learning model, search thepossible configuration settings for a target configuration setting thatresults in the performance metric meeting defined bounds of anoptimization threshold value.
 2. The system of claim 1, furthercomprising: a tester that controls operation of the radio frequencyapparatus based on a plurality of test configuration settings identifiedby the tuner, wherein the target configuration setting is from theplurality of test configuration settings.
 3. The system of claim 2,wherein the radio frequency apparatus is an amplifier, filter, digitalsignal processor, radio frequency integrated circuit,micro-electro-mechanical system filter, or monolithic microwaveintegrated circuit.
 4. The system of claim 1, wherein the plurality oftest configuration settings modulate at least one parameter of theoutput signal or operating parameter of the radio frequency apparatus.5. The system of claim 4, wherein the at least one parameter of theoutput signal includes: amplitude variation, rise time, fall time, pulsewidth, output power, in-band spectral emissions, out-of-band spectralemissions, error vector magnitude, or a combination thereof.
 6. Thesystem of claim 4, wherein the at least one operating parameter of theradio frequency apparatus includes: filter coefficient, output power, ora combination thereof.
 7. The system of claim 1, wherein the performancemetric is a function of performance evaluation data that characterizesthe output signal or the operating parameter of the radio frequencyapparatus.
 8. The system of claim 7, wherein the tuner determines theperformance metric by comparing the performance evaluation data to atarget performance dataset.
 9. The system of claim 7, wherein the testerdetermines the performance metric by executing a loss functionalgorithm, and wherein the defined bounds of the optimization thresholdis a range less than or equal to a defined loss value.
 10. The system ofclaim 7, wherein the loss function algorithm is a correlation-based lossfunction algorithm or an error-based loss function algorithm.
 11. Thesystem of claim 1, wherein the machine learning engine executes aBayesian optimization algorithm to identify the plurality of testconfiguration settings based on historic performance metrics thatcharacterize previous output signals generated by the radio frequencyapparatus in response to operations controlled by the tester.
 12. Thesystem of claim 1, wherein the tester is a computer executable componentstored in a computer readable storage medium comprised within the radiofrequency apparatus.
 13. The system of claim 1, wherein the tester sendsthe historic performance metrics to the tuner and receives the pluralityof test configuration settings from the tuner via a cloud computingenvironment.
 14. The system of claim 1, wherein the machine learningengine comprises computer executable components that include aninitialization component that selects an initial configuration settingfrom the plurality of possible configuration settings; and wherein thesystem further comprises a tester that controls operation of the radiofrequency apparatus in accordance with the initial configurationsetting.
 15. The system of claim 14, wherein the initializationcomponent randomly selects the initial configuration setting.
 16. Thesystem of claim 14, wherein the computer executable components furtherinclude a model update components that tunes a hyperparameter of themachine learning model based on the performance metric thatcharacterizes the output generated from a previously testedconfiguration setting.
 17. The system of claim 14, wherein the computerexecutable components further include a candidate component that selectsa test configuration setting based on the tuned machine learning model,and wherein the tester further controls the operation of the radiofrequency apparatus in accordance with the test configuration setting.18. The system of claim 1, wherein the target configuration settingoptimizes the radio frequency apparatus for use in a time-divisionalmultiple access digital communications network.
 19. Acomputer-implemented method for tuning a configuration setting of aradio frequency apparatus, the computer-implemented method comprising:applying a machine learning model to generate a test configurationsetting for the radio frequency apparatus; generating performanceevaluation data by operating the radio frequency apparatus with the testconfiguration setting; and comparing the performance evaluation data toa target performance dataset to determine whether the test configurationsetting is an optimal configuration setting for a defined objective. 20.The computer-implemented method of claim 19, wherein the machinelearning model is a regression model that defines a plurality ofprobabilistic relationships between parameters of the radio frequencyapparatus that are controllable via the test configuration setting andpredicted performance data.
 21. The computer-implemented method of claim20, wherein the machine learning model is a Gaussian process model or aRandom Forest model, and wherein the applying the machine learning modelis performed by a machine learning engine executing a Bayesianoptimization algorithm.
 22. The computer-implemented method of claim 19,further comprising: determining that the test configuration setting issub-optimal based on a performance metric that characterizes performancedata being less than the optimization threshold; generating an updatedmachine learning model by adjusting one or more hyperparameters based onthe performance evaluation data and the test configuration setting; andexecuting the updated machine learning model to generate a second testconfiguration setting for the radio frequency apparatus.
 23. Thecomputer-implemented method of claim 22, further comprising: generatingthe performance metric by executing a loss function algorithm thatcompares the performance evaluation data to the target performancedataset.
 24. The computer-implemented method of claim 23, wherein theloss function algorithm is a correlation-based loss function algorithmor an error-based loss function algorithm.
 25. The computer-implementedmethod of claim 19, wherein the radio frequency apparatus is anamplifier, filter, digital signal processor, radio frequency integratedcircuit, micro-electro-mechanical system filter, or monolithic microwaveintegrated circuit.
 26. The computer-implemented method of claim 19,wherein the test configuration setting modulates an output signal oroperating parameter of the radio frequency apparatus characterized bythe performance evaluation data.
 27. A computer program product fortuning configuration settings of a radio frequency apparatus, thecomputer program product comprising a computer readable storage mediumhaving program instructions embodied therewith, the program instructionsexecutable by one or more processors to cause the one or more processorsto: control an operation of the radio frequency apparatus using aninitial configuration setting; update a machine learning model based onperformance evaluation data characterizing the operation of the radiofrequency apparatus; and determine a test configuration setting for theradio frequency apparatus based on a prediction generated by the machinelearning model regarding a second operation of the radio frequencyapparatus using the test configuration setting.
 28. The computer programproduct of claim 27, wherein the machine learning model is a Gaussianprocess model or a Random Forests model.
 29. The computer programproduct of claim 27, wherein the program instructions further cause theone or more processors to: execute the second operation of the radiofrequency apparatus using the test configuration setting; collectadditional performance evaluation data charactering the second operationof the radio frequency apparatus; and compare the additional performanceevaluation data to an optimization threshold to determine whether thetest configuration setting is an optimal configuration setting.
 30. Thecomputer program product of claim 27, wherein the program instructionsfurther cause the one or more processors to: select the testconfiguration setting based on one or more performance constraintsregarding the radio frequency apparatus.
 31. The computer programproduct of claim 27, wherein the program instructions further cause theone or more processors to: generate the initial configuration settingbased on historic performance evaluation data from a third operation ofa second radio frequency apparatus.
 32. The computer program product ofclaim 27, wherein the test configuration setting modulates at least oneparameter of an output signal or the operation of the radio frequencyapparatus.
 33. The computer program product of claim 32, wherein the atleast one parameter of the output signal includes: amplitude variation,rise time, fall time, pulse width, output power, in-band spectralemissions, out-of-band spectral emissions, error vector magnitude, or acombination thereof.
 34. The computer program product of claim 33,wherein the at least one parameter of the operation of the radiofrequency apparatus includes: filter coefficient, output power, or acombination thereof.
 35. The computer program product of claim 27,wherein the program instructions further cause the one or moreprocessors to: execute a loss function algorithm to generate aperformance metric that compare the performance evaluation data to atarget performance data set.
 36. The computer program product of claim27, wherein the machine learning model is updated by fitting the machinelearning model to historic data that includes the performance evaluationdata.
 37. The computer program product of claim 27, wherein the testconfiguration setting optimizes the radio frequency apparatus for use ina time-divisional multiple access digital communications network.